Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import yfinance as yf
 import pandas as pd
-import matplotlib.pyplot as plt
+import plotly.graph_objects as go
 
 # Function to calculate Bollinger Bands
 def bollinger_bands(stock_price, window_size, num_of_std):
@@ -26,29 +26,34 @@ if st.sidebar.button('Analyze'):
     data = yf.download(ticker_symbol, start=start_date, end=end_date)
     data['Middle Band'], data['Upper Band'], data['Lower Band'] = bollinger_bands(data['Adj Close'], window_size, num_of_std)
 
-    # Plotting
-    fig, ax = plt.subplots()
-    ax.plot(data.index, data['Adj Close'], label='Adjusted Close', color='blue')
-    ax.plot(data.index, data['Middle Band'], label='Middle Band', linestyle='--', color='red')
-    ax.plot(data.index, data['Upper Band'], label='Upper Band', linestyle='--', color='green')
-    ax.plot(data.index, data['Lower Band'], label='Lower Band', linestyle='--', color='green')
+    # Plotting with Plotly
+    fig = go.Figure()
     
+    # Add traces for stock price and Bollinger Bands
+    fig.add_trace(go.Scatter(x=data.index, y=data['Adj Close'], mode='lines', name='Adjusted Close'))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Upper Band'], fill=None, mode='lines', name='Upper Band', line=dict(color='green')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Middle Band'], fill='tonexty', mode='lines', name='Middle Band', line=dict(color='red')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Lower Band'], fill='tonexty', mode='lines', name='Lower Band', line=dict(color='green')))
+
     # Buy and sell signals
-    buy_signal = (data['Adj Close'] < data['Lower Band'])
-    sell_signal = (data['Adj Close'] > data['Upper Band'])
-    ax.scatter(data.index[buy_signal], data['Adj Close'][buy_signal], color='gold', label='Buy Signal', marker='^', alpha=1)
-    ax.scatter(data.index[sell_signal], data['Adj Close'][sell_signal], color='purple', label='Sell Signal', marker='v', alpha=1)
-
-    plt.title('Bollinger Bands for {}'.format(ticker_symbol))
-    plt.xlabel('Date')
-    plt.ylabel('Price')
-    plt.legend()
-    st.pyplot(fig)
+    buys = data[data['Adj Close'] < data['Lower Band']]
+    sells = data[data['Adj Close'] > data['Upper Band']]
+    fig.add_trace(go.Scatter(x=buys.index, y=buys['Adj Close'], mode='markers', name='Buy Signal', marker=dict(color='gold', size=10, symbol='triangle-up')))
+    fig.add_trace(go.Scatter(x=sells.index, y=sells['Adj Close'], mode='markers', name='Sell Signal', marker=dict(color='purple', size=10, symbol='triangle-down')))
+
+    # Update layout for a more interactive look
+    fig.update_layout(title=f'Bollinger Bands for {ticker_symbol}',
+                      xaxis_title='Date',
+                      yaxis_title='Price',
+                      hovermode='x')
+
+    st.plotly_chart(fig)
 else:
     st.title("Bollinger Bands Trading Strategy App")
     st.markdown("""
     This app retrieves stock data using the ticker symbol entered by the user and applies the Bollinger Bands trading strategy to visualize potential buy and sell points. Adjust the parameters using the sidebar and click "Analyze" to see the results.
-    * **Python libraries:** yfinance, pandas, matplotlib
+    * **Python libraries:** yfinance, pandas, plotly
     * **Data source:** Yahoo Finance
+    * **Tip:** Hover over the plot to see interactive data points.
     """)